The information required for construction quantities surveying is not only generated by various participants in different construction phases but also stored in different forms including graphics, text, tables, or various combinations of the three. To report a bill of quantities (BOQ), the project manager has to continuously excerpt information from various resources and record it on papers. Without adequate staff and time, this repetitive and tedious process is difficult for the project manager to handle properly and thus reduces the effectiveness and the accuracy of the quantities surveying process which creates problems during the design, tender, and construction supervision of construction projects for designers and contractors pract

 In this research the Cobalt Oxide (Co3O4) films are prepared by the method of chemical spray pyrolysis deposition at different thicknesses such that (250, 350, 450, and 550) ± 20 nm.      The optical measurement shows that the Co3O4 films have a direct energy gap, and they in general increase with the increase of the thickness. The optical constants are investigated and calculated such as absorption coefficient, refractive index, extinction coefficient and the dielectric constants for the wavelengths in the range (300-900) nm.     The electrical conductivity (σ) and the activation energies (Ea1, Ea2) have been investigated on (Co3O4) thin films as a function of thickness. The films

Thin films of ZnSxSe1-x with different sulfide content(x)
(0, 0.02, 0.04, 0.06, 0.8, and 0.1), thickness (t) (0.3, 0.5, and 0.7 μm) and annealing temperature (Ta) (R.T 373 and 423K) were fabricated by thermal evaporating under vacuum of 10-5 Toor on glass substrate. The results show that the increasing of sulfide content (x)and annealing temperature lead to decrease the d.c conductivity σDC of and concentration of charge carriers (nH) but increases the activation energy (Ea1,Ea2), while the increasing of t increases σDC and nH but decrease (Ea1,Ea2). The results were explained in different terms

The electrical properties of polycrystalline cadmium telluride thin films of different thickness (200,300,400)nm deposited by thermal evaporation onto glass substrates at room temperature and treated at different annealing temperature (373, 423, 473) K are reported. Conductivity measurements have been showed that the conductivity increases from 5.69X10-5 to 0.0011, 0.0001 (?.cm)-1 when the film thickness and annealing temperature increase respectively. This increasing in ?d.c due to increasing the carrier concentration which result from the excess free Te in these films.

Thin films of GexS1-x were fabricated by thermal evaporating under vacuum of 10-5Toor on glass substrate. The effect of increasing of germanium content (x) in sulfide films on the electrical properties like d.c conductivity (σDC), concentration of charge carriers (nH) and the activation energy (Ea) and Hall effect were investigated. The measurements show that (Ea) increases with the increasing of germanium content from 0.1to0.2 while it get to reduces with further addition, while charge carrier density (nH) is found to decrease and increase respectively with germanium content. The results were explained in terms of creating and eliminating of states in the band gap

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

Estimation the unknown parameters of a two-dimensional sinusoidal signal model is an important and a difficult problem , The importance of this model  in modeling Symmetric gray- scale texture image . In this paper, we propose employment Deferential Evaluation algorithm and the use of Sequential approach to estimate the unknown frequencies and amplitudes of the 2-D sinusoidal components when the signal is affected by noise. Numerical simulation are performed for different sample size, and various level of standard deviation to observe the performance of this method in estimate the parameters of 2-D sinusoidal signal model , This model was used for modeling  the Symmetric gray scale texture image and estimating by using

Electrical properties were studied for Pectin/PVA graphene composites films and the effect of aqueous interaction on their properties. The conductivity and the dielectric constant of this composite are important because Polysaccharide like pectin is increasingly being used in biomedical applications and as nanoparticles coating materials. The Dielectric and conductivity of composite films were compared in dry and wet condition the differences in the results were attributed to the water molecules and the hydrogen bond which connect the three composite compounds (Pectin, PVA and Graphene) together. These connections were allowed the hydrogen and hydroxyl group’s migrations in the composite super molecules. On the other hand, graphene was pr

ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data

Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. This paper proposes a novel traffic scheduling algorithm to avoid congestion in the control plane. The Packet-In messages received from different 5G devices are classified into two classes: critical and non-critical 5G communication by adopting Dual-Spike Neural Networks (DSNN) classifier and implementing it on a Virtualized Network Function (VNF). Dual spikes identify each class to increase the reliability of the classification